The particle swarm algorithm lacks efficient parameter control when dealing with the optimization problems

and its easy to get into a local optimal and it causes a low convergence. A new kind of particle swarm optimization algorithm is proposed. Based on the adaptive value of particle

the algorithm adaptively adjusts the value of inertia weight and learning factor in the algorithm

in which the inertial weight is reduced by the nonlinear exponential

which is beneficial to the global search and local searching ability of the balanced algorithm

so that the algorithm can avoid falling into local extreme values. The learning factor adopts the strategy of asynchronous change to enhance the learning ability of the algorithm and improve the performance of the algorithm. The results of numerical experiments show that compared with SPSO and PSO-DAC algorithms

the improved algorithm has a significant improvement in convergence speed

stability and convergence accuracy.